2026 PESGM
Fine-Tuning LLMs to Generate Economical and Reliable Actions for the Power Grid
A fine-tuning framework for language models that generate corrective power-grid actions while balancing cost and reliability.
Research
02 / Research direction
Data-Driven Reliability and Verification
Stress testing, verification, and input-space construction for learned models used in constrained settings.
This direction focuses on making model behavior inspectable before learned components enter reliability-critical workflows.
Papers
Related work in this direction
4 papers connected to this topic.
2026 HICSS
NEO-Grid: A Neural Approximation Framework for Optimization and Control in Distribution Grids
A neural digital-twin approach for approximating distribution-grid physics while keeping optimization and control tractable.
2025 ICML
LEVIS: Large Exact Verifiable Input Spaces for Neural Networks
A verification method for constructing large exact input spaces over which neural-network behavior can be certified.
2024 NAPS
Should We Use Model-Free or Model-Based Control? A Case Study of Battery Control
A case study comparing model-free and model-based control choices for battery control problems.